11 results about "Demosaicing" patented technology

This invention provides an image conversion system and method between Bayer format and quad Bayer format. The system includes an image acquisition module that acquires an original Bayer format image and an original quad Bayer format image; a re-mosaic module that converts the original quad Bayer format image and the generated quad Bayer format image into a Bayer format image; a de-mosaic module that converts the original Bayer format image and the generated Bayer format image into a quad Bayer format image; and an image output module that outputs the generated quad Bayer format image and the Bayer format image. By unifying the re-mosaic and de-mosaic processes within the same system framework, the correlation between Bayer and quad Bayer formats can be better captured, and information loss during format conversion can be effectively reduced. The repeated re-mosaic and de-mosaic processes can also enhance each other, improving the conversion accuracy and quality, thus solving the problem of poor performance in existing Bayer and quad Bayer format image conversion methods.

This disclosure relates to an image processing apparatus, an image processing method, a program, and an imaging apparatus that can suppress the degradation of image quality. The image processing apparatus includes a demosaicing unit that performs demosaicing on an image generated by an image sensor having a first pixel array arranged in an oblique grid pattern, the first pixel receiving light of a first color component consisting of red, green, and blue or yellow color components; a second pixel receiving light of a second color component consisting of red color components; a third pixel receiving light of a third color component consisting of green color components; and a fourth pixel receiving light of a fourth color component consisting of blue color components; or a second pixel array arranged in an oblique grid pattern, the first pixel, second pixel, third pixel, fourth pixel, and a fifth pixel receiving light of a fifth color component different from the first to fourth color components. The technology of this disclosure can be applied, for example, to an image processing apparatus that performs demosaicing.

The present disclosure provides methods, devices, systems, and computer-readable media for de-mosaicking images by a device. A method includes obtaining a plurality of mosaicked images from a plurality of sensors of a device, concatenating each image of the plurality of mosaicked images with an encoded embedding of a respective mosaicking pattern of a sensor of the plurality of sensors that captured the each image of the plurality of mosaicked images, providing the concatenated plurality of mosaicked images to a machine learning model, and obtaining a plurality of de-mosaicked images corresponding to the plurality of mosaicked images from the machine learning model. Each sensor of the plurality of sensors has a respective mosaicking pattern from a plurality of mosaicking patterns.

The application provides a demosaicing and motion detection method and system based on a hybrid event camera, comprising: acquiring RAW images and event stream data; performing demosaicing processing on the RAW images based on a pyramid squeeze attention to obtain RGB images and shared features; performing motion detection on the shared features and the event stream data based on a lightweight recursive residual network to obtain a detection result; and integrating the RGB images and the detection result to output target image data. The demosaicing processing by the pyramid squeeze attention can effectively improve the edge features of dynamic targets; the motion detection on the shared features by the lightweight recursive residual network can not only improve the feature utilization rate, avoid repeated feature extraction, improve the system synergy, but also effectively improve the detection confidence, adapt to various scenes, realize lightweight deployment, and solve the problems of poor synergy, low feature utilization rate, and insufficient lightweight of the existing demosaicing and motion detection method of the hybrid event camera.

This invention relates to the field of image processing technology and discloses a joint processing architecture and method for RAW domain epipolar correction and demosaicing. The method includes the following steps: acquiring pairs of original RAW format images obtained by a binocular camera and generating left-eye RAW format image data and right-eye RAW format image data; constructing an adaptive depth RAW domain circular buffer and generating left-eye RAW buffer data and right-eye RAW buffer data; simultaneously, in a single processing pipeline, extracting local RAW feature window data in parallel based on the integer coordinate data of the original image, and performing color restoration by combining the demosaic interpolation weight data generated by edge-aware decision. This method can directly complete the joint calculation of edge-aware demosaicing processing and geometric resampling in the RAW domain, reducing the color artifacts and neighborhood information mismatch problems introduced by performing two independent resampling operations on RAW data and RGB images in the traditional process.

This invention discloses a controllable pixel displacement-driven joint demosaicing and super-resolution reconstruction method, comprising: 1. Constructing an original image sequence acquisition system consisting of a piezoelectric displacement module, a control module, and an image acquisition module, acquiring multiple frames of original image sequences through controllable sub-pixel displacement; 2. Constructing a joint demosaicing and super-resolution reconstruction network based on the multiple frame sequences, performing feature extraction and fusion on the multiple frame sequences; 3. Introducing a direction-aware wavelet enhancement module into the network, enhancing edge texture expression through wavelet decomposition, sub-band directional modulation, and reconstruction; 4. Introducing a structure-aware convolutional kernel attention module into the network, adaptively adjusting the convolutional kernel response according to local structural complexity to suppress false colors and structural distortion. This invention utilizes known displacement information to improve the determinism of original image sequence fusion and the detail fidelity and color consistency in high-frequency regions, and is applicable to scenarios such as industrial camera visual inspection and precision measurement.

The application discloses an image processing method and system based on a sub-true value image multi-loss function, relates to the technical field of artificial intelligence and image processing, and is characterized in that the de-mosaic network model used by the image processing method is trained based on a loss function of a sub-true value image and a loss function of a true value image. Specifically, a first loss value between a predicted image and a true value image is calculated; a plurality of sub-true value images are obtained according to the predicted image and the true value image; a plurality of second loss values between the predicted image and the sub-true value images are respectively calculated; and a final loss value is obtained according to the first loss value and the plurality of second loss values to optimize network parameters of a de-mosaic network structure, thereby avoiding the defects that a plurality of loss functions are based on one true value image, one or more loss functions are invalid, or training is trapped in a local optimal point, and the quality of a de-mosaic image is improved.

The embodiments of the present application provide an image processing method, an electronic device, a storage medium, and a program product, the image processing method comprising: extracting a first channel image from a first RAW image; merging a first Gr channel image and a first Gb channel image to obtain a first G channel image; and combining a first R channel image, a first G channel image, and a first B channel image to obtain a first RGB image, the first RGB image serving as a training reference image for first training, and the first training referring to training a first target model to perform demosaicing processing and denoising processing. The described solution directly constructs an RGB image on the basis of a RAW image, which lowers the likelihood of defects such as image artifacts and color artifacts appearing in images, helps to improve generation quality of training data, and thus improves training effectiveness for image signal processing models.

The present invention provides a video control device, a video recording device, a video control method, a video recording method, and a video control program that can perform imaging with an appropriate exposure value depending on whether or not pixel data is recorded before demosaicing. [Solution] The temporary storage unit 106 temporarily stores the pixel data output from the imaging unit 119. The development processing unit 107 outputs video data obtained by performing development processing, including demosaicing, on the pixel data stored in the temporary storage unit 106. The output control unit 109 switches between a first recording mode in which only the video data is recorded as the video data and a second recording mode in which both the video data and the pixel data are recorded as the video data. The output control unit 109 also controls the imaging unit 119 to perform imaging with different exposure values ​​in the first recording mode and the second recording mode.

The application discloses an underwater image de-scattering enhancement method based on active polarization sampling and modal complementary reasoning, and belongs to the technical field of intelligent visual processing. The method comprises the following steps: an image of a target underwater scene is collected by a polarization camera to obtain a polarization original image; a light residual convolutional neural network is constructed to perform de-mosaic processing on the polarization original image to obtain a polarization image after de-mosaic processing; polarization prior information is constructed based on the polarization image after de-mosaic processing; a light external enhancement model trained by knowledge distillation is constructed by taking a total intensity image as input to obtain external enhancement prior information; a modal complementary reasoning network is constructed, and joint reasoning is performed on the polarization prior information and the external enhancement prior information to obtain an initial de-scattering image; Gamma correction is performed on the initial de-scattering image to obtain a final enhancement image; underwater image quality multi-dimensional evaluation is performed on the final enhancement image; and the enhancement image after evaluation is provided to a downstream task module for calling.